Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS<sup>®</sup>

Lamine, Salim; Petropoulos, George P.; Singh, Sudhir Kumar; Szabó, Szilárd; Bachari, Nour El Islam; Srivastava, Prashant K.; Suman, Swati

doi:10.1080/10106049.2017.1307460

Cited by 95 publications

(48 citation statements)

References 55 publications

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“…It can help users to quantify the structure of landscapes. The landscape subject to analysis is user-defined and can represent any spatial phenomenon [35]. Based on the features of the study area, we emphasized on the quantity, shape features, aggregation, and diversity to analyze the spatial pattern of the green landscape [36].…”

Section: Landscape Pattern Analysismentioning

confidence: 99%

Spatial-Temporal Dynamic Analysis of Land Use and Landscape Pattern in Guangzhou, China: Exploring the Driving Forces from an Urban Sustainability Perspective

Liu

Chen³

2019

Sustainability

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Rapid urbanization is one of the most important factors causing land-use change, which mainly results from the orientation of government policies, adjustment of industrial structure, and migration of the rural population. Land use and land cover change (LUCC) is the natural foundation of urban development that is significantly influenced by human activities. By analyzing the LUCC and its inner driving force, as well as landscape pattern change, human activity and urban sustainable development can be better understood. This research adopted a geographic information system (GIS) and remote sensing (RS) technology to comprehensively analyze land use of Guangzhou, respectively, in 1995, 2005, and 2015. Fragmentation Statistics (FRAGSTATS) is the most authoritative software to calculate landscape metrics. Landscape pattern change was analyzed by FRAGSTATS. The results showed that urban land significantly increased from 16.33% in 1995 to 36.05% in 2015. Farmland greatly decreased from 45.16% in 1995 to 27.82% in 2005 and then slightly decreased to 25.10% in 2015. In the first decade, the non-agricultural conversion of rural land and the expansion of urban land was the dominant factor that led to the change. In the second decade, urban land had been supplemented through the redevelopment of low-efficiency land. The fragmentation of landscape patterns significantly increased from 1995 to 2005 and slightly decreased from 2005 to 2015. It indicated that the change in land use in the second decade was different from that in the first. This difference mainly resulted from three aspects: (1) urban development area and ecological conservation area were clearly defined in Guangzhou; (2) many small towns had developed into urban centers, and the scattered urban land gathered into these centers; (3) the establishment of greenway improved the connection of fragmented patches. After that, this study discussed land-use change and its causes and proposed the trend of urban development from the perspective of sustainability.

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Section: Landscape Pattern Analysismentioning

confidence: 99%

Spatial-Temporal Dynamic Analysis of Land Use and Landscape Pattern in Guangzhou, China: Exploring the Driving Forces from an Urban Sustainability Perspective

Liu

Chen³

2019

Sustainability

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“…The metrics used for the analysis are of a universal nature and are commonly used in landscape research (e.g. Lamine et al 2017;Marco da Silva et al 2015;Chefaoui 2014). Furthermore, they are easily interpretable, which is very important if the results are to be disseminated among a wide group of readers.…”

Section: Methodsmentioning

confidence: 99%

Effectiveness of Natura 2000 areas for environmental protection in 21 European countries

Kubacka

Smaga

2019

Reg Environ Change

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To assess the dynamics of changes in CORINE Land Cover classes in areas of the Natura 2000 ecological network, three landscape metrics were examined. Traditional pixel-based classification, with majority rules aggregation based on the example of the CORINE Land Cover dataset, was applied. To identify the possible differences in the considered metrics, according to the year as well as to the country, statistical analysis between the linear mixed model and the variance model with repeated measurements was performed. The results of both tests are very similar. In the 1990-2012 period, the share of CORINE Land Cover class "Artificial areas" in all tested areas increased by 21.1% (the highest growth ratios were recorded in Poland, Portugal, Slovenia and in Spain). On the other hand, such countries as Slovakia, Romania, Germany, Lithuania and Estonia are characterised by the loss of artificial areas. At the same time, the share of "Forest and semi-natural areas" as well as "Water bodies" increased slightly. Negative trends that took place in the periods 1990-2000 and 2000-2006 were effectively stopped in the subsequent period, 2006-2012. Overall, for all the analysed countries, a minimal loss in environmental and landscape diversity was observed. Our results may be used as a basis for drawing conclusions on the effectiveness of environmental and landscape management systems in various countries. They might also constitute the starting point for detailed analysis of the management process.

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“…SVM applies optimization algorithms to determine the location of ideal boundaries that can most effectively distinguish between classes (Lamine et al, 2018). An object-based SVM classification method was tested by Teo and Wu (2017) to categorize multispectral ALS data into five classes, achieving an overall accuracy of 96%.…”

Section: Comparisonmentioning

confidence: 99%

Multispectral Airborne Laser Scanning Point-Clouds for Land Cover Classification Using Convolutional Neural Networks

Ma¹,

Chen²,

Li³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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This paper presents an automated workflow for pixel-wise land cover (LC) classification from multispectral airborne laser scanning (ALS) data using deep learning methods. It mainly contains three procedures: data pre-processing, land cover classification, and accuracy assessment. First, a total of nine raster images with different information were generated from the pre-processed point clouds. These images were assembled into six input data combinations. Meanwhile, the labelled dataset was created using the orthophotos as the ground truth. Also, three deep learning networks were established. Then, each input data combination was used to train and validate each network, which developed eighteen LC classification models with different parameters to predict LC types for pixels. Finally, accuracy assessments and comparisons were done for the eighteen classification results to determine an optimal scheme. The proposed method was tested on six input datasets with three deep learning classification networks (i.e., 1D CNN, 2D CNN, and 3D CNN). The highest overall classification accuracy of 97.2% has been achieved using the proposed 3D CNN. The overall accuracy (OA) of the 2D and 3D CNNs was, on average, 8.4% higher than that of the 1D CNN. Although the OA of the 2D CNN was at most 0.3% lower than that of the 3D CNN, the runtime of the 3D CNN was five times longer than the 2D CNN. Thus, the 2D CNN was the best choice for the multispectral ALS LC classification when considering efficiency. The results demonstrated the proposed methods can successfully classify land covers from multispectral ALS data.

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Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS^®

Cited by 95 publications

References 55 publications

Spatial-Temporal Dynamic Analysis of Land Use and Landscape Pattern in Guangzhou, China: Exploring the Driving Forces from an Urban Sustainability Perspective

Spatial-Temporal Dynamic Analysis of Land Use and Landscape Pattern in Guangzhou, China: Exploring the Driving Forces from an Urban Sustainability Perspective

Effectiveness of Natura 2000 areas for environmental protection in 21 European countries

Multispectral Airborne Laser Scanning Point-Clouds for Land Cover Classification Using Convolutional Neural Networks

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Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®

Cited by 95 publications

References 55 publications

Spatial-Temporal Dynamic Analysis of Land Use and Landscape Pattern in Guangzhou, China: Exploring the Driving Forces from an Urban Sustainability Perspective

Spatial-Temporal Dynamic Analysis of Land Use and Landscape Pattern in Guangzhou, China: Exploring the Driving Forces from an Urban Sustainability Perspective

Effectiveness of Natura 2000 areas for environmental protection in 21 European countries

Multispectral Airborne Laser Scanning Point-Clouds for Land Cover Classification Using Convolutional Neural Networks

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Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS^®